Journal of Inverse and Ill-posed Problems
Editor-in-Chief: Kabanikhin, Sergey I.
6 Issues per year
IMPACT FACTOR 2016: 0.783
5-year IMPACT FACTOR: 0.792
CiteScore 2016: 0.80
SCImago Journal Rank (SJR) 2015: 0.583
Source Normalized Impact per Paper (SNIP) 2015: 1.106
Mathematical Citation Quotient (MCQ) 2015: 0.43
Optimal control as a regularization method for ill-posed problems
- Department of Mathematics, UCLA, on leave from Industrial Mathematics Institute, University Linz, Altenbergerstr. 69, 4040 Linz, Austria. E-mail: email@example.com
- Department of Mathematics, University of California, Los Angeles, CA 90095-1555, USA. E-mail: firstname.lastname@example.org
We describe two regularization techniques based on optimal control for solving two types of ill-posed problems. We include convergence proofs of the regularization method and error estimates. We illustrate our method through problems in signal processing and parameter identification using an efficient Riccati solver. Our numerical results are compared to the same examples solved using Tikhonov regularization.
Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.